Most use of sentiment analysis in social media to date has been extremely limited. Analytics with dashboards full of traffic light symbols gloss only the most obvious features of social conversations, often obscuring the real reactions and trends which move opinion about a product or a company. In this presentation, we discuss the causes, both technical and human, behind the failure of early sentiment approaches. We will introduce the technologies and practices for advanced conversation analytics, and show how understanding tone and context for commentary provides a far more accurate analytical frame for decision-making around social media.
LoudWhistle Branding International is all about modern brand building.
Services offered:
I. Branding:
Consultation
Brand Experience
Creative Direction
II. Creative Designs
Web Development
Blog Design
Interactive Presentations
Graphic Designs
III. Marketing
Lead Generation Tools
(Brochures, Corporate Profile, Landing Pages, Microsites)
Social Media Marketing
Social Networking
Share your story with us!
hello@loudwhistle.net
Twitter: brandteller
This document lists locations in several European countries that are mentioned in the song "Fairytale" by Alexander Rybak, including castles and other landmarks in Germany, Italy, the Czech Republic, the Netherlands, Belgium, France, Scotland, Switzerland, England, Portugal, and Spain. It concludes by informing the listener that they can continue listening to the music or press 'Esc' to exit.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms for those who already suffer from conditions like anxiety and depression.
This document provides guidelines for Worksheet No. 1, which requires students to submit a short research article summarizing the results of a survey. The article should be clipped or downloaded in full along with the source identified. Students must highlight the population, sample, and parameters of the survey; enumerate at least five descriptive statements and the qualitative and quantitative data gathered; and submit the worksheet in a specified format. The rubric evaluates students based on inclusion of required elements, accuracy, and timeliness of submission.
This document discusses the use of statistics and probabilities in corpus linguistics. It explains that statistics can provide useful tools for linguists to better understand languages. Probabilities in particular can be used to estimate word frequencies and develop probabilistic models of spelling. The document also discusses best practices for annotating corpora, including annotating with sufficient data to achieve statistical significance and avoiding errors like testing machine learning models on the same data they were trained on.
This document defines and describes different types of data used in data analysis and research projects. It discusses nominal, ordinal, interval, ratio, continuous, and discontinuous data. For each data type, examples are provided and key characteristics are outlined, such as whether mathematical properties and statistical tests can be performed on that data. Common scaling techniques for measuring attitudes, like Likert scales, semantic differential scales, and graphic rating scales are also introduced.
The document discusses various steps involved in analyzing and interpreting data, including developing an analysis plan, collecting and cleaning data, analyzing the data using appropriate techniques, interpreting the results by drawing conclusions and recommendations while also considering limitations. It provides examples of different analysis techniques like descriptive statistics, inferential statistics, and qualitative data analysis and emphasizes the importance of interpreting data in the context of the research questions.
Naive Bayes classifiers are a simple yet effective method for sentiment analysis and text classification problems. They work by calculating the probability of a document belonging to a certain class based on the presence of individual words or features, assuming conditional independence between features given the class. This allows probabilities to be estimated efficiently from training data. While the independence assumption is often unrealistic, naive Bayes classifiers generally perform well compared to more sophisticated approaches. The document discusses various techniques for preprocessing text like tokenization, stemming, part-of-speech tagging, and negation handling to improve the accuracy of naive Bayes classifiers for sentiment analysis tasks.
LoudWhistle Branding International is all about modern brand building.
Services offered:
I. Branding:
Consultation
Brand Experience
Creative Direction
II. Creative Designs
Web Development
Blog Design
Interactive Presentations
Graphic Designs
III. Marketing
Lead Generation Tools
(Brochures, Corporate Profile, Landing Pages, Microsites)
Social Media Marketing
Social Networking
Share your story with us!
hello@loudwhistle.net
Twitter: brandteller
This document lists locations in several European countries that are mentioned in the song "Fairytale" by Alexander Rybak, including castles and other landmarks in Germany, Italy, the Czech Republic, the Netherlands, Belgium, France, Scotland, Switzerland, England, Portugal, and Spain. It concludes by informing the listener that they can continue listening to the music or press 'Esc' to exit.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms for those who already suffer from conditions like anxiety and depression.
This document provides guidelines for Worksheet No. 1, which requires students to submit a short research article summarizing the results of a survey. The article should be clipped or downloaded in full along with the source identified. Students must highlight the population, sample, and parameters of the survey; enumerate at least five descriptive statements and the qualitative and quantitative data gathered; and submit the worksheet in a specified format. The rubric evaluates students based on inclusion of required elements, accuracy, and timeliness of submission.
This document discusses the use of statistics and probabilities in corpus linguistics. It explains that statistics can provide useful tools for linguists to better understand languages. Probabilities in particular can be used to estimate word frequencies and develop probabilistic models of spelling. The document also discusses best practices for annotating corpora, including annotating with sufficient data to achieve statistical significance and avoiding errors like testing machine learning models on the same data they were trained on.
This document defines and describes different types of data used in data analysis and research projects. It discusses nominal, ordinal, interval, ratio, continuous, and discontinuous data. For each data type, examples are provided and key characteristics are outlined, such as whether mathematical properties and statistical tests can be performed on that data. Common scaling techniques for measuring attitudes, like Likert scales, semantic differential scales, and graphic rating scales are also introduced.
The document discusses various steps involved in analyzing and interpreting data, including developing an analysis plan, collecting and cleaning data, analyzing the data using appropriate techniques, interpreting the results by drawing conclusions and recommendations while also considering limitations. It provides examples of different analysis techniques like descriptive statistics, inferential statistics, and qualitative data analysis and emphasizes the importance of interpreting data in the context of the research questions.
Naive Bayes classifiers are a simple yet effective method for sentiment analysis and text classification problems. They work by calculating the probability of a document belonging to a certain class based on the presence of individual words or features, assuming conditional independence between features given the class. This allows probabilities to be estimated efficiently from training data. While the independence assumption is often unrealistic, naive Bayes classifiers generally perform well compared to more sophisticated approaches. The document discusses various techniques for preprocessing text like tokenization, stemming, part-of-speech tagging, and negation handling to improve the accuracy of naive Bayes classifiers for sentiment analysis tasks.
Improving search with neural ranking methodsvoginip
Neural ranking methods have improved search quality but also have limitations. The good is that neural methods using contextual word embeddings have significantly boosted search accuracy. However, the bad is that no single neural approach works best for all queries. Additionally, the ugly is that neural methods rely on learned word similarities that can reflect societal biases and fall back on statistical correlations rather than true understanding.
Search as Communication: Lessons from a Personal JourneyDaniel Tunkelang
The document discusses lessons learned from the author's personal journey in search engineering. It covers insights from library science about treating search as an information-seeking context and communicating with users. It also discusses the importance of entity detection and how to leverage corpus features to improve extraction. The author realized that queries vary in difficulty and systems need to recognize this and adapt accordingly. The key takeaway is that search should be treated as a communication problem rather than just a ranking task.
Reshaping Scientific Knowledge Dissemination and Evaluation in the Age of the...Aliaksandr Birukou
This talk tries to unveil some of the problems inherent in the current knowledge creation, dissemination, and evaluation practices, also based on models and quantitative analyses of the effectiveness of peer review as gatekeeping/assessment method and of citations as measure of impact. The speaker will present the recent research and development threads aiming at making the knowledge generation and dissemination process efficient, and the evaluation process (more) fair and accurate. He will in particular present the models and tools being developed to this end, which are essentially based on applying to knowledge dissemination the lessons learned from open source development and the social web. The presentation will be interactive and discussion-oriented.
Collective Opinion Spam Detection Bridging Review Networks and MetadataShebuti Rayana
1) The document proposes SpEagle and SpLite, unsupervised collective classification approaches for detecting opinion spam in online reviews.
2) SpEagle uses loopy belief propagation on a Markov random field to jointly classify users, reviews, and products as spam or legitimate. SpLite provides a computationally efficient version.
3) Experiments on Yelp datasets show SpEagle and SpLite outperform existing methods, and incorporating limited labels further improves performance while maintaining efficiency.
Validation and mechanism: exploring the limits of evaluationAlan Dix
Talk at Evaluation, SummerPIT 2019, Aarhus University, 15th August 2019
https://alandix.com/academic/talks/PIT-2019-validation-and-mechanism/
Sometimes evaluation is straightforward. Perhaps our goal is to create a system in a well-understood environment that is fastest to use or with least errors. In this case, and if we believe design choices are effectively independent, then we can run a lab or in-situ study to compare design alternatives. However many things do not fit into this easy-to-evaluate category. Sometimes our goals or more diffuse or long term: sustainability, behavioural change, improving education. Sometimes the thing we wish to 'evaluate' is 'generative' such as toolkits or frameworks used by developers or designers to create systems that then are used by others. In these cases simple post-hoc 'try it and measure it' approaches to evaluation fail, or at best give partial results. However post-hoc evaluation is only one way to validate work – data (quantitative or qualitative) should be combined with an understanding of mechanism, how things work, in order to justify, generalise and innovate.
Social text sentiment and tone analysis [aai 201] - (4160)Ruben Pertusa Lopez
The document discusses social text sentiment and tone analysis using advanced analytics and insights. It provides an agenda for a session that will help attendees understand how to analyze sentiment and tone using cutting edge Microsoft technologies like HDInsight and SQL Server. The session will provide an overview of sentiment analysis, demonstrate how to gather and store social data, and review different sentiment analysis techniques.
This document summarizes Jillian Aurisano's Master of Science defense presentation about her work developing a new visualization called BactoGeNIE to enable comparative gene neighborhood analysis across large numbers of bacterial genomes. The visualization is designed to scale to "big data" and "big displays" by using a high-density compressed representation of genomes without text labels. It encodes gene orthology using coordinated color and alignment, and allows experts to interactively sort, query, and compare gene neighborhoods across many genomes to gain biological insights.
The document provides an overview of the examinable skills and structure for the A2 Crime and Deviance exam. [1] It outlines the assessment objectives focusing on knowledge and understanding (AO1) and application, interpretation, analysis and evaluation (AO2). [2] Example questions are provided testing AO1 and AO2 for crime and deviance topics as well as methods. [3] The document aims to prepare students for the exam by familiarizing them with the skills and topics that will be assessed.
Directly e-mailing authors of newly published papers encourages community curation, by Stephanie Bunt, Gary Grumbling, Helen Field, Steven Marygold, Thom Kaufman, Kathy Matthews, Nick Brown and Gillian Millburn.
Presented at the 5th International Biocuration Conference, hosted by PIR in Washington, DC, April 2-4, 2012.
Dr. Lani discusses all aspects of the dissertation methodology, including: selecting a survey instrument, population, reliability, validity, data analysis plan, and IRB/URR considerations.
This document summarizes a presentation about the future of scientific information and communication given by Antony Williams at SUNY Potsdam on April 12th 2013. The presentation discusses how the internet and online platforms are influencing scientists and the way scientific work is conducted and shared. It explores how scientists are building online profiles and becoming "quantified" based on various online metrics. The presentation envisions a future where all historical scientific data is mapped and integrated online in an open and collaborative manner to enable new discoveries.
Improving Your Literature Reviews with NVivo 10 for WindowsQSR International
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Neural ranking methods have improved search quality but also have limitations. The good is that neural methods using contextual word embeddings have significantly boosted search accuracy. However, the bad is that no single neural approach works best for all queries. Additionally, the ugly is that neural methods rely on learned word similarities that can reflect societal biases and fall back on statistical correlations rather than true understanding.
Search as Communication: Lessons from a Personal JourneyDaniel Tunkelang
The document discusses lessons learned from the author's personal journey in search engineering. It covers insights from library science about treating search as an information-seeking context and communicating with users. It also discusses the importance of entity detection and how to leverage corpus features to improve extraction. The author realized that queries vary in difficulty and systems need to recognize this and adapt accordingly. The key takeaway is that search should be treated as a communication problem rather than just a ranking task.
Reshaping Scientific Knowledge Dissemination and Evaluation in the Age of the...Aliaksandr Birukou
This talk tries to unveil some of the problems inherent in the current knowledge creation, dissemination, and evaluation practices, also based on models and quantitative analyses of the effectiveness of peer review as gatekeeping/assessment method and of citations as measure of impact. The speaker will present the recent research and development threads aiming at making the knowledge generation and dissemination process efficient, and the evaluation process (more) fair and accurate. He will in particular present the models and tools being developed to this end, which are essentially based on applying to knowledge dissemination the lessons learned from open source development and the social web. The presentation will be interactive and discussion-oriented.
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Validation and mechanism: exploring the limits of evaluationAlan Dix
Talk at Evaluation, SummerPIT 2019, Aarhus University, 15th August 2019
https://alandix.com/academic/talks/PIT-2019-validation-and-mechanism/
Sometimes evaluation is straightforward. Perhaps our goal is to create a system in a well-understood environment that is fastest to use or with least errors. In this case, and if we believe design choices are effectively independent, then we can run a lab or in-situ study to compare design alternatives. However many things do not fit into this easy-to-evaluate category. Sometimes our goals or more diffuse or long term: sustainability, behavioural change, improving education. Sometimes the thing we wish to 'evaluate' is 'generative' such as toolkits or frameworks used by developers or designers to create systems that then are used by others. In these cases simple post-hoc 'try it and measure it' approaches to evaluation fail, or at best give partial results. However post-hoc evaluation is only one way to validate work – data (quantitative or qualitative) should be combined with an understanding of mechanism, how things work, in order to justify, generalise and innovate.
Social text sentiment and tone analysis [aai 201] - (4160)Ruben Pertusa Lopez
The document discusses social text sentiment and tone analysis using advanced analytics and insights. It provides an agenda for a session that will help attendees understand how to analyze sentiment and tone using cutting edge Microsoft technologies like HDInsight and SQL Server. The session will provide an overview of sentiment analysis, demonstrate how to gather and store social data, and review different sentiment analysis techniques.
This document summarizes Jillian Aurisano's Master of Science defense presentation about her work developing a new visualization called BactoGeNIE to enable comparative gene neighborhood analysis across large numbers of bacterial genomes. The visualization is designed to scale to "big data" and "big displays" by using a high-density compressed representation of genomes without text labels. It encodes gene orthology using coordinated color and alignment, and allows experts to interactively sort, query, and compare gene neighborhoods across many genomes to gain biological insights.
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Beyond Sentiment Hype: Conversation Context for Accurate Discovery
1. Beyond
Sen)ment
Hype:
Conversa)on
Context
for
Accurate
Discovery
Hadley
Reynolds
NextEra
Research
2. Agenda
• Where
we
are
now
–
market
drivers
&
technology
dynamics
• The
Sen)ment
Bubble
considered
• Differen)a)ng
levels
of
analysis
• Prac)cal
dimensions
of
analysis
and
examples
• Discussion
7. Market
Drivers
for
Sen)ment
Analysis
Addi$onal
Web
2.0
Content:
Blogs
Discussion
Forums
Amazon
(Yelp,
Trip
Advisor
etc.)
Reviews
User
Generated
RaAngs
Data
“Like”
Google+
And
more,
much
more…
13. Challenges
for
Sen)ment
Analysis
• Level
of
analysis
• Timeframes
for
analysis
• Rela)ve
sophis)ca)on
of
analysis
14. Level
of
Analysis
• Corpus
(Do
the
bloggers
like
us?)
• Document
(Does
this
author
like
us?)
15. Document
Sen)ment
Math
Posi)ve
document
=
4
points
or
above
Nega)ve
document
=
-‐2
points
or
below
Neutral
document
=
-‐2
through
+3
good
Value
Score
great
Term
good
2
2
o.k.
great
3
3
o.k.
1
1
disappointed
-‐4
-‐4
Total:
+2
disappointed
Neutral
Document
16. Document
Sen)ment
Math
Posi)ve
document
=
4
points
or
above
Nega)ve
document
=
-‐2
points
or
below
Product
A
Product
B
Neutral
document
=
-‐2
through
+3
good
ok
good
Value
Score
great
Term
ok
Product
A
good
2
2
o.k.
Product
A
great
3
3
Product
A
o.k.
1
1
Product
A
-‐4
-‐4
disappointed
Product
B
good
1
1
disappointed
Product
B
ok
1
2
disappointed
Product
B
-‐4
-‐8
disappointed
disappointed
Nega)ve
Document
Total:
-‐3
17. Level
of
Analysis
• Corpus
(Do
the
bloggers
like
us?)
• Document
(Does
this
author
like
us?)
• Sentence
(What
is
this
person’s
comment?)
• En)ty/A`ribute
(What
is
it
about
us
that
she
likes
or
doesn’t
like?)
18. En)ty-‐level
Analysis
Sources
Person
Opinion
Target
En)ty
(Feature)
(Profile)
Person
(Emo)on)
Opinion
(Feature)
Target
En)ty
(Feature)
(Social
Network)
20. Sophis)ca)on
of
Analysis
• Keyword-‐based
sen)ment
techniques
– Sen)ment
terms:
elusive,
ambiguous,
in
flux
– Sen)ment
lexicons:
incomplete,
non-‐specific,
inflexible
– Unable
to
understand
context
surrounding
an
expression
or
the
people
contribu)ng
– Unable
to
understand
connec)ons
among
related
en))es
and
a`ributes
and
people
– Unable
to
gauge
quality
of
source
materials
21. Sophis)ca)on
of
Analysis
• Seman)c-‐based
sen)ment
techniques
– Sen)ment
terms
>>
incorporate
related
expressions,
fuzzy
logic
-‐
NLP
– Sen)ment
lexicons
>>
domain
ontologies
(available
or
buildable)
provide
analy)cal
context
– Able
to
understand
context
surrounding
an
expression
or
the
people
contribu)ng
-‐
machine
learning
&
other
techniques
– Able
to
understand
connec)ons
among
related
en))es
and
a`ributes
and
people
-‐
triples,
event
extrac)on
22. Dimensions
of
Analysis
• Ontologies
around
opinion
objects
• Iden)fica)on
and
qualifica)on
of
en))es
&
a`ributes
&
rela)onships
• Emo)onal
content
of
expression(s)
• Quality
gauge
of
sources
• Profiles
of
individual
commenters
• Roles/interac)ons/sociology
of
commenters
and
their
affilia)ons
• Timeframe
for
expressions
and
responses
23. Beyond
+/-‐:
Ontology-‐based
analy)cs
Same
Ontology
breakdown
Same
Scale:
Expressed
Opinions
Higher
values
for
cardiovascular
diseases
with
Avas)n
Source:
BuzzStory
25. Quality
of
Content
Sources
topix.com
cancergrace.org
• Quality:
4.48
• Quality:
16.78
"I
know
of
one
method
that
"As
shown
above,
a
total
of
would
be
really
scary
and
362
pa)ents
who
hadn't
graphic
that
would
work
progressed
aser
first
line
towards
gepng
people
to
chemo/Avas)n
were
stop
pollu)ng
my
sea
breeze
randomized
to
either
of
the
environment.
two
maintenance
therapy
What
I
wonna
know
is
they
arms,
and
the
combina)on
keep
pupng
down
smokers
arm
showed
a
significantly
and
blaming
us
for
longer
progression-‐free
evrything.”
survival
(PFS)
coun)ng
from
the
beginning
of
all
treatment,
at
10.”
26. Affilia)on
Network
–
Map
of
Affilia)ons
of
People
&
Topics
Supplements
Tobacco
Addic)on
Prostate
Cancer
Breast
Cancer
Co-‐Morbidi)es
Thyroid
Disease
Biomarkers
Lung
Cancer
Targeted
Therapies
Chemotherapy
H&N
Cancer
Source:
BuzzStory
27. Sociology
of
Affilia)ons
&
Topic
Groupings
Co-‐Morbidi)es
Tobacco
Addic)on
Other
Types
of
Cancer
Supplements
Misc.
Side-‐Effects
Misc.
Side-‐Effects
Biomarkers
Source:
BuzzStory
29. Challenges
Remain
“The
service
at
Reynards
is,
in
general,
friendly
and
loose.
Though
they
couldn’t
find
a
reserva)on
for
four
one
Friday
night,
they
compensated
with
so
much
warmth
and
comped
wine
that
all
was
forgiven.
In
some
ways,
Reynards
offers
what
one
wishes
a
dining
experience
in
Manha`an
would
be:
kindness
instead
of
aptude,
inoffensive
prices,
glorious
food,
and
aesthe)c
variety—the
clientele
is
split
roughly
in
half
between
the
stylish
and
the
schlumpy.”
The
New
Yorker,
September
24,
2012
30. Resources
• Bing
Liu,
Sen$ment
Analysis
and
Opinion
Mining,
Morgan
&
Claypool,
2012
• Bo
Pang
and
Lillian
Lee,
Opinion
Mining
and
Sen$ment
Analysis,
(Founda$ons
and
Trends
in
Informa$on
Retrieval),
Now
Publishers,
2008
• Sen)ment
Analysis
Symposium,
San
Francisco,
CA,
October
30,
2012